Repositorio Institucional de la Universidad de Lima

El Repositorio Institucional Ulima centraliza, preserva y difunde en acceso abierto la producción académica, científica y cultural de la Universidad de Lima.

Repositorio Institucional de la Universidad de Lima

El Repositorio Institucional Ulima centraliza, preserva y difunde en acceso abierto la producción académica, científica y cultural de la Universidad de Lima.

Tesis de bachiller

Cardano cryptocurrency price from Twitter. A prediction algorithm from machine learning

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Resumen
Cryptocurrencies are a growing market that has attracted the attention of many investors in recent years. While cryptocurrencies offer a secure and decentralized form of payment, this market is highly volatile. Factors influencing price changes include the balance of supply and demand, its utility, trading indicators, and market confidence. The present research aims to predict the price of the Cardano cryptocurrency by using machine learning techniques, specifically SVM, LSTM and BiLSTM models. In addition to accounting for financial indices, Twitter activity was used as a data source to measure market sentiment. The study analyzes various predictive horizons, including time ranges of 1 day, seven days, 14 days, 21 days and 30 days. The results obtained were validated with different performance indicators, and it was determined that the model predicts Cardano prices one month ahead with a MAPE of less than 22%, providing valuable information for investors interested in the volatile Cardano cryptocurrency market.
Editor
Universidad de Lima
Fecha
2024
Cita bibliográfica

Piccarreta Acosta, R., & Zavala Arana, A. (2024). Cardano cryptocurrency price from Twitter. A prediction algorithm from machine learning [Tesis de bachiller, Universidad de Lima]. https://hdl.handle.net/20.500.12724/21708

Piccarreta Acosta R, Zavala Arana A. Cardano cryptocurrency price from Twitter. A prediction algorithm from machine learning [Tesis de bachiller]. Universidad de Lima; 2024. Disponible en: https://hdl.handle.net/20.500.12724/21708

@misc{20.500.12724/21708,
	author = "Piccarreta Acosta, Riccardo and Zavala Arana, Alejandra",
	title = "Cardano cryptocurrency price from Twitter. A prediction algorithm from machine learning",
	publisher = "Universidad de Lima",
	year = "2024",
	note = "Tesis de bachiller",
	url = "https://hdl.handle.net/20.500.12724/21708",
}
  • dc.contributor.advisor
    Quiroz Flores, Juan Carlos
  • dc.contributor.author
    Piccarreta Acosta, Riccardo
    Zavala Arana, Alejandra
  • dc.date.accessioned
    2024-12-12T14:41:52Z
  • dc.date.available
    2024-12-12T14:41:52Z
  • dc.date.issued
    2024
  • dc.description.abstract
    Cryptocurrencies are a growing market that has attracted the attention of many investors in recent years. While cryptocurrencies offer a secure and decentralized form of payment, this market is highly volatile. Factors influencing price changes include the balance of supply and demand, its utility, trading indicators, and market confidence. The present research aims to predict the price of the Cardano cryptocurrency by using machine learning techniques, specifically SVM, LSTM and BiLSTM models. In addition to accounting for financial indices, Twitter activity was used as a data source to measure market sentiment. The study analyzes various predictive horizons, including time ranges of 1 day, seven days, 14 days, 21 days and 30 days. The results obtained were validated with different performance indicators, and it was determined that the model predicts Cardano prices one month ahead with a MAPE of less than 22%, providing valuable information for investors interested in the volatile Cardano cryptocurrency market.
    en_GB
    Cryptomonedas son un mercado creciente que ha atraído la atención de varios inversionistas en los últimos años. Si bien las criptomonedas ofrecen una forma de pago segura y descentralizada, este mercado es muy volátil. Los factores que influyen en los cambios de precios incluyen el equilibrio de la oferta y la demanda, su utilidad, los indicadores de trading y la confianza del mercado. El objetivo de la presente investigación es predecir el precio de la criptomoneda Cardano mediante el uso de técnicas de aprendizaje automático, específicamente modelos de SVM, LSTM y BiLSTM. Además de tomar en cuenta índices financieros, se utilizó como fuente de datos la actividad en Twitter para medir la opinión del mercado. El estudio analiza diversos horizontes predictivos, incluyendo rangos de tiempo de 1 día, 7 días, 14 días, 21 días y 30 días. Los resultados obtenidos fueron validados con distintos indicadores de desempeño y se determinó que el modelo predice precios de Cardano con un mes de anticipación con un MAPE menor al 22% brindando información valiosa para los inversores interesados en el mercado volátil de la criptomoneda Cardano.
    es_PE
  • dc.format
    application/pdf
  • dc.identifier.citation
    Piccarreta Acosta, R., & Zavala Arana, A. (2024). Cardano cryptocurrency price from Twitter. A prediction algorithm from machine learning [Tesis para optar el Título Profesional de Ingeniero Industrial, Universidad de Lima]. Repositorio Institucional de la Universidad de Lima. https://hdl.handle.net/20.500.12724/21708
    es_PE
  • dc.identifier.isni
    121541816
  • dc.identifier.uri
    https://hdl.handle.net/20.500.12724/21708
  • dc.language.iso
    eng
  • dc.publisher
    Universidad de Lima
  • dc.publisher.country
    PE
  • dc.rights
    info:eu-repo/semantics/openAccess
  • dc.rights.uri
    https://creativecommons.org/licenses/by-nc-sa/4.0/
  • dc.source
    Repositorio Institucional - Ulima
    Universidad de Lima
  • dc.subject
    Algoritmos
    es_PE
    Aprendizaje automático
    es_PE
    Criptomonedas
    es_PE
    Twitter
    es_PE
  • dc.subject.ocde
    https://purl.org/pe-repo/ocde/ford#2.11.04
  • dc.title
    Cardano cryptocurrency price from Twitter. A prediction algorithm from machine learning
    en_GB
  • dc.type
    info:eu-repo/semantics/bachelorThesis
  • dc.type.other
    Tesis
  • renati.advisor.dni
    10300185
  • renati.advisor.orcid
    https://orcid.org/0000-0003-1858-4123
  • renati.author.dni
    60191081
    76612634
  • renati.discipline
    722026
  • renati.juror
    Corzo Chávez, Jorge Antonio
    Tupia De la Cruz, Elmer Luis
    Quiroz Flores, Juan Carlos
  • renati.level
    https://purl.org/pe-repo/renati/level#tituloProfesional
  • renati.type
    https://purl.org/pe-repo/renati/type#tesis
  • thesis.degree.discipline
    Ingeniería Industrial
    es_PE
  • thesis.degree.grantor
    Universidad de Lima. Facultad de Ingeniería
  • thesis.degree.level
    Título Profesional
  • thesis.degree.name
    Ingeniero Industrial
  • ulima.cat
    OI
ALICIA
Renati
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